Myeloid-derived suppressor cell (MDSC) key genes analysis in rat anti-CD28-induced immune tolerance kidney transplantation

BackgroundIn the field of transplantation, inducing immune tolerance in recipients is of great importance. Blocking co-stimulatory molecule using anti-CD28 antibody could induce tolerance in a rat kidney transplantation model. Myeloid-derived suppressor cells (MDSCs) reveals strong immune suppressive abilities in kidney transplantation. Here we analyzed key genes of MDSCs leading to transplant tolerance in this model.MethodsMicroarray data of rat gene expression profiles under accession number {"type":"entrez-geo","attrs":{"text":"GSE28545","term_id":"28545"}}GSE28545 in the Gene Expression Omnibus (GEO) database were analyzed. Running the LIMMA package in R language, the differentially expressed genes (DEGs) were found. Enrichment analysis of the DEGs was conducted in the Database for Annotation, Visualization and Integrated Discovery (DAVID) database to explore gene ontology (GO) annotation and their Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Their protein-protein interactions (PPIs) were provided by STRING database and was visualized in Cytoscape. Hub genes were carried out by CytoHubba.ResultsThree hundred and thirty-eight DEGs were exported, including 27 upregulated and 311 downregulated genes. The functions and KEGG pathways of the DEGs were assessed and the PPI network was constructed based on the string interactions of the DEGs. The network was visualized in Cytoscape; the entire PPI network consisted of 192 nodes and 469 edges. Zap70, Cdc42, Stat1, Stat4, Ccl5 and Cxcr3 were among the hub genes.ConclusionsThese key genes, corresponding proteins and their functions may provide valuable background for both basic and clinical research and could be the direction of future studies in immune tolerance, especially those examining immunocyte-induced tolerance.     

Altered chromatin landscape in circulating T follicular helper and regulatory cells following grass pollen subcutaneous and sublingual immunotherapy

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Analysis of pharmacological mechanisms of Yinyanghuo as treatment of erectile dysfunction with network pharmacology-based strategy

Erectile dysfunction is considered an important health problem that impacts the quality of life of men. Yinyanghuo, also called Epimedium or Horny Goat Weed, is a frequently used Chinese traditional herbal medicine, commonly used in treating erectile dysfunction in China. A network pharmacology method was performed systematically, at a molecular level, to analyse the pharmacological mechanism of Yinyanghuo as erectile dysfunction therapy. The network pharmacology method used in this study primarily includes prescreening of the active compounds, prediction of targets, network analysis and gene enrichment analysis. This network analysis proved that 4 targets (AR, NR3C2, PDE5A and BMP2) could be the targets of Yinyanghuo therapy on erectile dysfunction. Besides, gene enrichment analysis predicted that Yinyanghuo might have a role in erectile dysfunction by regulating 10 molecular functions, 8 cellular components, 10 biological processes and 36 possible targets related to 10 signalling pathways. Our study demonstrated the molecular and pharmacological mechanisms of Yinyanghuo against erectile dysfunction with a holistic approach and demonstrated a powerful method for analysing pharmacological mechanisms and rational utilisation of Traditional Chinese Medicine clinically.     

Increases in synthetic cannabinoids-related harms: Results from a longitudinal web-based content analysis

BackgroundSynthetic Cannabinoid Receptor Agonists (SCRA), also known as “K2” or “Spice,” have drawn considerable attention due to their potential of abuse and harmful consequences. More research is needed to understand user experiences of SCRA-related effects. We use semi-automated information processing techniques through eDrugTrends platform to examine SCRA-related effects and their variations through a longitudinal content analysis of web-forum data.MethodEnglish language posts from three drug-focused web-forums were extracted and analyzed between January 1st 2008 and September 30th 2015. Search terms are based on the Drug Use Ontology (DAO) created for this study (189 SCRA-related and 501 effect-related terms). EDrugTrends NLP-based text processing tools were used to extract posts mentioning SCRA and their effects. Generalized linear regression was used to fit restricted cubic spline functions of time to test whether the proportion of drug-related posts that mention SCRA (and no other drug) and the proportion of these “SCRA-only” posts that mention SCRA effects have changed over time, with an adjustment for multiple testing.Results19,052 SCRA-related posts (Bluelight (n = 2782), Forum A (n = 3882), and Forum B (n = 12,388)) posted by 2543 international users were extracted. The most frequently mentioned effects were “getting high” (44.0%), “hallucinations” (10.8%), and “anxiety” (10.2%). The frequency of SCRA-only posts declined steadily over the study period. The proportions of SCRA-only posts mentioning positive effects (e.g., “High” and “Euphoria”) steadily decreased, while the proportions of SCRA-only posts mentioning negative effects (e.g., “Anxiety,” ‘Nausea,” “Overdose”) increased over the same period.ConclusionThis study’s findings indicate that the proportion of negative effects mentioned in web forum posts and linked to SCRA has increased over time, suggesting that recent generations of SCRA generate more harms. This is also one of the first studies to conduct automated content analysis of web forum data related to illicit drug use.     

GeneYenta: A Phenotype­Based Rare Disease Case Matching Tool Based on Online Dating Algorithms for the Acceleration of Exome Interpretation

Advances in next‐generation sequencing (NGS) technologies have helped reveal causal variants for genetic diseases. In order to establish causality, it is often necessary to compare genomes of unrelated individuals with similar disease phenotypes to identify common disrupted genes. When working with cases of rare genetic disorders, finding similar individuals can be extremely difficult. We introduce a web tool, GeneYenta, which facilitates the matchmaking process, allowing clinicians to coordinate detailed comparisons for phenotypically similar cases. Importantly, the system is focused on phenotype annotation, with explicit limitations on highly confidential data that create barriers to participation. The procedure for matching of patient phenotypes, inspired by online dating services, uses an ontology­based semantic case matching algorithm with attribute weighting. We evaluate the capacity of the system using a curated reference data set and 19 clinician entered cases comparing four matching algorithms. We find that the inclusion of clinician weights can augment phenotype matching.